Comparative Study of AI-Based Academic Performance Prediction Models in University of Jos, Plateau State
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- NGN 5000
Background of the study
The prediction of academic performance has become a key area of interest in educational research, particularly with the advent of machine learning and AI technologies. Traditional methods of academic performance prediction primarily rely on manual assessments, which can be inaccurate and inconsistent. AI-powered systems, however, have the potential to revolutionize this process by analyzing large datasets, recognizing patterns, and providing more accurate predictions. These systems use student data such as past grades, attendance, participation, and even behavioral factors to predict future academic outcomes. This study will compare the effectiveness of AI-based academic performance prediction models with traditional methods at the University of Jos, Plateau State, aiming to determine which method provides more accurate and reliable predictions.
Statement of the problem
At the University of Jos, Plateau State, predicting student academic performance is largely based on historical data and manual assessments. This method has proven to be limited in accuracy, often overlooking key factors that could influence a student’s performance. The introduction of AI-based predictive models could address this issue by using a broader range of data and providing more accurate predictions. However, there is little research comparing the effectiveness of AI-based models with traditional prediction methods in Nigerian universities. This study aims to fill this gap by assessing the performance of AI-powered models at the University of Jos.
Objectives of the study
1. To develop AI-based academic performance prediction models using student data at the University of Jos.
2. To compare the accuracy and reliability of AI-based models with traditional academic performance prediction methods.
3. To evaluate the potential benefits of using AI-based models in academic performance prediction at the University of Jos.
Research questions
1. How accurate are AI-based academic performance prediction models compared to traditional methods?
2. What factors contribute to the accuracy of AI-based models in predicting student performance?
3. How can AI-based performance prediction models be integrated into university academic advising systems?
Research hypotheses
1. AI-based academic performance prediction models will provide more accurate predictions compared to traditional methods.
2. The use of AI models will allow for the identification of key factors that influence student academic performance.
3. AI-powered performance prediction systems will lead to improved student interventions and outcomes compared to traditional methods.
Significance of the study
This study will contribute valuable insights into the comparative effectiveness of AI-powered academic performance prediction models in Nigerian universities. The findings will provide a basis for universities to adopt more accurate predictive models, potentially improving student support and academic outcomes.
Scope and limitations of the study
The study will focus on developing and evaluating AI-based academic performance prediction models at the University of Jos, Plateau State. Limitations include potential challenges in data collection, as well as the need for accurate and comprehensive student data for training the AI models.
Definitions of terms
• AI-Based Prediction Model: A machine learning algorithm that uses historical student data to predict future academic performance.
• Traditional Prediction Methods: Conventional methods of predicting academic performance based on manual assessments or simple statistical models.
• Accuracy: The degree to which the prediction models correctly forecast student performance outcomes.
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